{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import explained_variance_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_all_file = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/data/prediction_compare_step36/test_step36_pre72_smoothed1.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1943"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2303-360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53.97222222222222"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1943/36"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(test_all_file,header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>350</th>\n",
       "      <th>351</th>\n",
       "      <th>352</th>\n",
       "      <th>353</th>\n",
       "      <th>354</th>\n",
       "      <th>355</th>\n",
       "      <th>356</th>\n",
       "      <th>357</th>\n",
       "      <th>358</th>\n",
       "      <th>359</th>\n",
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       "      <th>0</th>\n",
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       "      <td>14.527366</td>\n",
       "      <td>...</td>\n",
       "      <td>20.356927</td>\n",
       "      <td>21.964106</td>\n",
       "      <td>23.970088</td>\n",
       "      <td>26.141740</td>\n",
       "      <td>28.284783</td>\n",
       "      <td>30.903986</td>\n",
       "      <td>34.086655</td>\n",
       "      <td>37.072213</td>\n",
       "      <td>39.726844</td>\n",
       "      <td>41.772370</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.345636</td>\n",
       "      <td>6.288030</td>\n",
       "      <td>6.240025</td>\n",
       "      <td>6.366687</td>\n",
       "      <td>6.638906</td>\n",
       "      <td>6.865755</td>\n",
       "      <td>7.221463</td>\n",
       "      <td>7.684552</td>\n",
       "      <td>8.070460</td>\n",
       "      <td>8.392050</td>\n",
       "      <td>...</td>\n",
       "      <td>117.909641</td>\n",
       "      <td>117.758034</td>\n",
       "      <td>117.631695</td>\n",
       "      <td>117.693079</td>\n",
       "      <td>117.077566</td>\n",
       "      <td>116.731305</td>\n",
       "      <td>116.609421</td>\n",
       "      <td>116.507851</td>\n",
       "      <td>116.089876</td>\n",
       "      <td>114.741563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43.699373</td>\n",
       "      <td>46.249477</td>\n",
       "      <td>49.541231</td>\n",
       "      <td>53.117693</td>\n",
       "      <td>57.598077</td>\n",
       "      <td>61.831731</td>\n",
       "      <td>66.693109</td>\n",
       "      <td>71.244258</td>\n",
       "      <td>75.870215</td>\n",
       "      <td>79.891846</td>\n",
       "      <td>...</td>\n",
       "      <td>109.904278</td>\n",
       "      <td>110.420232</td>\n",
       "      <td>110.850193</td>\n",
       "      <td>111.208494</td>\n",
       "      <td>111.840412</td>\n",
       "      <td>112.533677</td>\n",
       "      <td>113.278064</td>\n",
       "      <td>113.731720</td>\n",
       "      <td>114.109767</td>\n",
       "      <td>114.091472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>112.398314</td>\n",
       "      <td>111.165262</td>\n",
       "      <td>110.471052</td>\n",
       "      <td>110.059210</td>\n",
       "      <td>109.716008</td>\n",
       "      <td>109.596673</td>\n",
       "      <td>109.830561</td>\n",
       "      <td>110.192134</td>\n",
       "      <td>110.826779</td>\n",
       "      <td>111.522315</td>\n",
       "      <td>...</td>\n",
       "      <td>121.933995</td>\n",
       "      <td>122.944996</td>\n",
       "      <td>123.787497</td>\n",
       "      <td>124.656247</td>\n",
       "      <td>125.546873</td>\n",
       "      <td>126.122394</td>\n",
       "      <td>127.101995</td>\n",
       "      <td>128.084996</td>\n",
       "      <td>128.570830</td>\n",
       "      <td>128.809025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>121.698335</td>\n",
       "      <td>122.081946</td>\n",
       "      <td>122.568288</td>\n",
       "      <td>123.306907</td>\n",
       "      <td>123.755756</td>\n",
       "      <td>123.963130</td>\n",
       "      <td>124.135941</td>\n",
       "      <td>124.446618</td>\n",
       "      <td>125.205515</td>\n",
       "      <td>125.671262</td>\n",
       "      <td>...</td>\n",
       "      <td>149.331362</td>\n",
       "      <td>150.109469</td>\n",
       "      <td>150.091224</td>\n",
       "      <td>149.576020</td>\n",
       "      <td>148.646683</td>\n",
       "      <td>147.705569</td>\n",
       "      <td>146.421308</td>\n",
       "      <td>144.517756</td>\n",
       "      <td>142.431464</td>\n",
       "      <td>139.692886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>135.690146</td>\n",
       "      <td>136.241788</td>\n",
       "      <td>137.201490</td>\n",
       "      <td>138.001242</td>\n",
       "      <td>139.334368</td>\n",
       "      <td>140.445307</td>\n",
       "      <td>141.371089</td>\n",
       "      <td>143.475907</td>\n",
       "      <td>145.896590</td>\n",
       "      <td>147.747158</td>\n",
       "      <td>...</td>\n",
       "      <td>72.470947</td>\n",
       "      <td>71.225789</td>\n",
       "      <td>69.854824</td>\n",
       "      <td>68.379020</td>\n",
       "      <td>66.982517</td>\n",
       "      <td>65.485431</td>\n",
       "      <td>64.071192</td>\n",
       "      <td>62.725993</td>\n",
       "      <td>61.438328</td>\n",
       "      <td>60.198607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>138.866658</td>\n",
       "      <td>136.222215</td>\n",
       "      <td>133.518513</td>\n",
       "      <td>131.265427</td>\n",
       "      <td>128.721189</td>\n",
       "      <td>125.934324</td>\n",
       "      <td>122.778604</td>\n",
       "      <td>119.982170</td>\n",
       "      <td>117.151808</td>\n",
       "      <td>114.126507</td>\n",
       "      <td>...</td>\n",
       "      <td>28.278756</td>\n",
       "      <td>27.398963</td>\n",
       "      <td>26.665803</td>\n",
       "      <td>25.554836</td>\n",
       "      <td>24.629030</td>\n",
       "      <td>23.690858</td>\n",
       "      <td>22.742382</td>\n",
       "      <td>21.785318</td>\n",
       "      <td>20.821098</td>\n",
       "      <td>19.850915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>58.174392</td>\n",
       "      <td>56.978660</td>\n",
       "      <td>55.982217</td>\n",
       "      <td>55.151847</td>\n",
       "      <td>54.293206</td>\n",
       "      <td>53.244338</td>\n",
       "      <td>52.036949</td>\n",
       "      <td>50.864124</td>\n",
       "      <td>49.553437</td>\n",
       "      <td>48.127864</td>\n",
       "      <td>...</td>\n",
       "      <td>7.042256</td>\n",
       "      <td>6.868547</td>\n",
       "      <td>6.723789</td>\n",
       "      <td>6.603158</td>\n",
       "      <td>6.502631</td>\n",
       "      <td>6.418859</td>\n",
       "      <td>6.349049</td>\n",
       "      <td>6.290875</td>\n",
       "      <td>6.242395</td>\n",
       "      <td>6.201996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.522754</td>\n",
       "      <td>17.768962</td>\n",
       "      <td>17.140801</td>\n",
       "      <td>16.450668</td>\n",
       "      <td>15.708890</td>\n",
       "      <td>15.090741</td>\n",
       "      <td>14.408951</td>\n",
       "      <td>13.840793</td>\n",
       "      <td>13.367327</td>\n",
       "      <td>12.806106</td>\n",
       "      <td>...</td>\n",
       "      <td>20.455663</td>\n",
       "      <td>22.046386</td>\n",
       "      <td>23.871988</td>\n",
       "      <td>25.893324</td>\n",
       "      <td>28.244436</td>\n",
       "      <td>31.037030</td>\n",
       "      <td>34.197525</td>\n",
       "      <td>37.664604</td>\n",
       "      <td>40.720504</td>\n",
       "      <td>43.100420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.249805</td>\n",
       "      <td>6.208171</td>\n",
       "      <td>6.173476</td>\n",
       "      <td>6.144563</td>\n",
       "      <td>6.287136</td>\n",
       "      <td>6.405947</td>\n",
       "      <td>6.671622</td>\n",
       "      <td>7.059685</td>\n",
       "      <td>7.383071</td>\n",
       "      <td>7.819226</td>\n",
       "      <td>...</td>\n",
       "      <td>121.376941</td>\n",
       "      <td>121.314117</td>\n",
       "      <td>120.928431</td>\n",
       "      <td>121.107026</td>\n",
       "      <td>120.755855</td>\n",
       "      <td>120.796546</td>\n",
       "      <td>121.497122</td>\n",
       "      <td>121.414268</td>\n",
       "      <td>121.178557</td>\n",
       "      <td>120.148797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 360 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0           1           2           3           4           5    \\\n",
       "0   22.389671   21.324726   20.437271   19.531059   18.609216   17.674347   \n",
       "1    6.345636    6.288030    6.240025    6.366687    6.638906    6.865755   \n",
       "2   43.699373   46.249477   49.541231   53.117693   57.598077   61.831731   \n",
       "3  112.398314  111.165262  110.471052  110.059210  109.716008  109.596673   \n",
       "4  121.698335  122.081946  122.568288  123.306907  123.755756  123.963130   \n",
       "5  135.690146  136.241788  137.201490  138.001242  139.334368  140.445307   \n",
       "6  138.866658  136.222215  133.518513  131.265427  128.721189  125.934324   \n",
       "7   58.174392   56.978660   55.982217   55.151847   54.293206   53.244338   \n",
       "8   18.522754   17.768962   17.140801   16.450668   15.708890   15.090741   \n",
       "9    6.249805    6.208171    6.173476    6.144563    6.287136    6.405947   \n",
       "\n",
       "          6           7           8           9    ...         350  \\\n",
       "0   16.895289   16.079407   15.232840   14.527366  ...   20.356927   \n",
       "1    7.221463    7.684552    8.070460    8.392050  ...  117.909641   \n",
       "2   66.693109   71.244258   75.870215   79.891846  ...  109.904278   \n",
       "3  109.830561  110.192134  110.826779  111.522315  ...  121.933995   \n",
       "4  124.135941  124.446618  125.205515  125.671262  ...  149.331362   \n",
       "5  141.371089  143.475907  145.896590  147.747158  ...   72.470947   \n",
       "6  122.778604  119.982170  117.151808  114.126507  ...   28.278756   \n",
       "7   52.036949   50.864124   49.553437   48.127864  ...    7.042256   \n",
       "8   14.408951   13.840793   13.367327   12.806106  ...   20.455663   \n",
       "9    6.671622    7.059685    7.383071    7.819226  ...  121.376941   \n",
       "\n",
       "          351         352         353         354         355         356  \\\n",
       "0   21.964106   23.970088   26.141740   28.284783   30.903986   34.086655   \n",
       "1  117.758034  117.631695  117.693079  117.077566  116.731305  116.609421   \n",
       "2  110.420232  110.850193  111.208494  111.840412  112.533677  113.278064   \n",
       "3  122.944996  123.787497  124.656247  125.546873  126.122394  127.101995   \n",
       "4  150.109469  150.091224  149.576020  148.646683  147.705569  146.421308   \n",
       "5   71.225789   69.854824   68.379020   66.982517   65.485431   64.071192   \n",
       "6   27.398963   26.665803   25.554836   24.629030   23.690858   22.742382   \n",
       "7    6.868547    6.723789    6.603158    6.502631    6.418859    6.349049   \n",
       "8   22.046386   23.871988   25.893324   28.244436   31.037030   34.197525   \n",
       "9  121.314117  120.928431  121.107026  120.755855  120.796546  121.497122   \n",
       "\n",
       "          357         358         359  \n",
       "0   37.072213   39.726844   41.772370  \n",
       "1  116.507851  116.089876  114.741563  \n",
       "2  113.731720  114.109767  114.091472  \n",
       "3  128.084996  128.570830  128.809025  \n",
       "4  144.517756  142.431464  139.692886  \n",
       "5   62.725993   61.438328   60.198607  \n",
       "6   21.785318   20.821098   19.850915  \n",
       "7    6.290875    6.242395    6.201996  \n",
       "8   37.664604   40.720504   43.100420  \n",
       "9  121.414268  121.178557  120.148797  \n",
       "\n",
       "[10 rows x 360 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 360)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_all = df.values\n",
    "data_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_week = data_all[0]\n",
    "len(two_week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4608"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for row in range(1, 119):\n",
    "    two_week = np.append(two_week, data_all[row][-36:])\n",
    "len(two_week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa8b65d3860>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(two_week[:288])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa8b675fa20>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(two_week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa8b682abe0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wZkTB7XBqD4Z3rLzvV17dcJjIlv4o7v7APkyvFvGvz7un9sVz7nXwxlojycZDA8xM6Hr22aW8VJt6gU+Snxisae4BQxd2lwMCtOV2qapiOGGuonTfEGegNs2qYeTuMnt55lcjYg7OUX3y+ALOrRTw81eNt3+xjlNzVCeXtfvbfC/V84ZLR3HllhQefN49fRI9515HV85d/7DPLrvPuf/mA88BgNGAixN3WGdtxkKD8x4LB1xp65y+uqufGhQP+1210mCM4dxyoekAjGjIOdnrsF4AeNfNuzv+GafqHnjzumYPSc512wZwfC7rmqImz7nXsZAtIRKsFf60YrwvAr+PcMaFkXuhoiAW8q+ZvgM4r7M2YyGr6ZprUjeDfpQVFWWXDJTgzOjOvT4AcFvkvpAto1RVmzqlqJ7t4QQvz2SwuS+yJjuqHUnHIvcChhPhtvLRtqE4sqUqlvPuSN/1nHsd85nSmsKfVgT8Pmzpj+Lskrva0maKFVQUhv/7jedv/kRdmNEB1FZMa5x72J1FVz85uYR4yI9dw+evitx0bs8ZyQGN5YSYg5H7yzMZXLKpeRfIRji1oXpupX3dC6AlWAA1SdRpPOdex3xd4U87JgajrovcTy9q9jRqWRx38IZuxUL2fFkm7tKiq5+eXMINu4bWDHIBtIeRm7Jl2skJ0aDfkfbPjDGcXsxj53D7LBkzTqXGnlspYGsHVeg7h7X77ZTn3N3JQqaMkW6c+0DM2HBxC/xhs63BBpCTOmsrFrIl+GhtW9qY0dzKPc5dVRnOLOWxu0Futtsi97PGRmBzzd2JVdFSroxCRWlqVzMSEfsj93JV1fYtOrB122Acfh/h+Jzn3F3JfLaE4Q42UzkTgzEsZMuuGgHWKnJ3MkOiFfOZEgbj4bWdC41RcO55GM1nSyhVVUw0WKbz/Qy3TLo6cGYF/bEgkk1G18VCzkTuZ/UVxdYmclEzkuEAsmV7J4mdWMiirKi4fLx5O2JOKODD9qEYXpnN2GBZezznbqKiqFjOl7uSZXjnSJ4f7wbOLOUwGA81vKmd1FlbUZ/jDrij/0k9PO21UVocL5F3w6SrmdUivvviDCKB5puAXJaxO7ujdg67j9wZA/I2nt+js1qOe6spUWYuG0/h8Iw7KlU9525iKVdeM/S4E3hRw5yLBk+fWco3lGQA3bm7KBLmzGfLa3LcAXMbXffYe6aFc3fTw4j3PPr9d1ze9DXRkOYsixV7s5Eme4zcjeZhNkozfFO62f1Uz6VjSZxdKrhidew5dxPd5LhzxvRcZzdF7qcX8w0lGcCdskxVUfHS1CrGUvU9x3XN3QWRMOfsUgFEjTcp3fQwOjKdho+AN1462vQ1RiM5m8/vifksBuMho0d7p9SGkNuXariQKSEa9Hc8lnKbft+5YbC759xN9OLceeQ+75LIvVxVMbVSwPYWkbsbZAMzj708j4rCsK+uc6Eb2+ieWcpjLBlpmPPMH0ZuaHZ2ZCaDHcPxlrnZ0aAzo/Z+eHQBN+4aav/COvioPTsLmRayJQwnQ+1fqMMLxiY95+4uDOfeheaeigYQDvhcE7l/+l9fhMqal0rHQn5UFOaqwqCXptIgAm7bu2XN8bgLB2BMLuebVnwaQ6ddYO8L51ZxhT5zoBm8kZydhUzFioKZdBGXjXeX4w6YI3c7nXt3e3BcappcWQfOnYjuJaI5InrBdOzPiOgIER0kom8SUb9+fAcRFYjogP7nbyTaLhw+c7S/i+nqRITRVNgVmvtqoYIv/+QMgPP7d3PcOI3plbkMJgZihrPhRF0YubeavBULuyMvfylXxtRqEa/a0jrDI+pAI7le9XagNmrPTs19IVvCULxz5z6SCCPk97kiPbqTyP0+AG+rO/Y9AFcyxq4C8AqAT5q+d5wxtlf/8+tizLQHHhHEu2y4P5aMOB65//DoPK7+g4cBAF/60Guwq0kbVUPqqLjHYR6fy57XAwfQUstCfvcMwGCMYWa12HS2Lnc+TmvuPBXvkiYD3jlODG8510VL7XqcmKOqjarsXJbx+QhbBqLrQ5ZhjD0OYKnu2MOMMX6GfwxgqwTbbCdfriIW8sPXYgp7I9wQuf/xQ9qYr+FEGK9rMbYs6rLccVVlOLmQa1gUBGjRsFs2gOczJZQVtalzd8sYw28d0DoT8mlhzTBWRrZG7lpE20zaaoXdc1QVlWEp113FOqBttl8oG6q/CuDfTP/eSUTPEdEPiOimZj9ERHcR0X4i2j8/Py/ADOtkS0pPY7IG46E1Df3tJlOsGLnDX/rQa1q+Nu4yWWYmXUSpqjbN7om7aADGL3zhRwCa72cYmruDMtLJhRweePoMfvG6rW2dUjxs/x7BueUCAj5q2Be9HdxeuzT35XwZKkPXzn00GXZFgoUl505EnwJQBfAl/dA0gG2MsWsA/BaALxNRw/CBMXY3Y2wfY2zfyMiIFTOEkS9XkQh3NjjAzGA8jJVCBYpDlYk/eGUemVIV//TrN7ZtxmTIMi6Jhnk17Y6heMPva9k9zttaqirGUvs1TTI9apq7cw+jf37mLPw+wu+89ZK2rzXGLtr4MDq9lMfm/uiaSuROCfp9iAR9tjl33u+oW+c+ktKcu9Otf3t27kR0J4CfB/BLTP9fMMZKjLFF/etnABwHcLEIQ+0gV6r2FrnHgmBMe9I7wZPHFpCMBHDNRH/b1zqxFG8F76DXtOhKcjOuuUwR/+VbLxg3cjNeOLcKAPib/3hd0/zskN+HgI+kPjgPT6fx4tRqw+/Npou478lTuHHXEEY7mCTmRGO2g5MruLLNRm8rEuGgbamQCxnehrpzzR3QNlXLiop0wdmgpCfnTkRvA/C7AN7JGMubjo8QkV//eheAPQBOiDDUDrKlateFFQAwqD/ZlxyQZqqKikcOz+F1u4cR8Lf/OJ1Yirfi5EIOIb+veQZKUK7m/tiROdz/1Gns+8NH8MOjzeXB/ae0is9mWUiAljkVC/mlPow+cM/T+Lm/fAJv/dzj50WGr/nvjyJXVnDb3s0dvZdRUWtTJDyXLuLsUgHXTDQ/h+2ws6e7Ebl3UfcC1OpknJ6n2kkq5AMAngJwCRFNEtEHAXweQBLA9+pSHm8GcJCIngfwzwB+nTG21PCNXUi+rBhL627gQ50Xs/Y79xen0pjPlHBrh+PKePqb05t+nOcnV3DRaKLpMj0elusszVHgB+55uuFrjs9n8cf/dgQTg9G2BW7xsLwKYD5nFgBens3gJydrt5Y5V72+XqAZdmcj3fPkSQDA9TsHe34Prae7PRWqvcoybilsbBumMsbuaHD4niav/TqAr1s1yimypWrLOYnNGNSduxOyDO8h8uodnUVDcRcNwJhZLeInJ5fwK6/d2fQ1stsl8NoGAGg2n+X3vqmVeHRyk8ckzn0t6YVnt75qEx46NIOTCzncoOv/R2a09Mcv/NK15/WZb0Us7JceuX/4y89iJV/GS1NpXDyWwJVbWhdXtcLOnu4L2TJCfh9Ske5W8zwAmG8j9cnGq1A1kS8pHY3Xq8eI3B2QZZ49s4LxvgjG+zpLLYu5SHN/8PlzYAz4wI3bm74mHpY7JDtdqKAvGsRv3qJNraoqayt38+Wqobf/t9uubPt+8XBAWrYMj86v2toPYG1keEi38VVbu3OcsrORZlaL+M7BaTx5bBHL+Qpu27ulp81UTiJi3xzVhWwJQ4lQR1PZzBiyTNpz7q4hV6p23CDIDJ9TuuSALPPs6WVcu61zDTMc8IFIbrbMH377JfzqfT9tmz30nUMzuHprH3YON86UAfTIXWKktlqoIBkJYCQZBmPAUt3q63svzSJTquLLH3pNRxGnzMid9wTqiwbRHwuu0XSPTKeRigTaDnGuJy4xci9VFXzgnp+sOdatffUkbY3cu89xB4BUJIBQwOdF7m6BMYZcubcN1aC+dFvK2fthzqWLOLdSwDXb+jv+GSJCXPKQ7L9/4iT+95E5/I9HjzZ9zUq+jIOTK3hDi66FgD7dSGLP8XxZQSIcMFZf5k3xmdUiPvqVAwCapz/WE5coI3EpLRr0YygeWmPr0dksLh5Ldh1lxsMBafsv33tpFkfnsmvaeTQrAOuUhM0bqt1mygDaPVb/+TiB59x1ihUVKkNPqZCAFr3bPfX80SNzAIBrW2RwNELmqL2KSdb42k/PNm1K9Y1nNUnmhjZOMxaW23O8UFEQCfqN8X7mG/IHr2jn973Xbe1YSoiFA9LaD/DIPRL0Y9DkPJ45vYynTy3h6g5SYevRZBk5znJmVVtZ/OMHa4V1zbKiOoUPybYjh3w+U+qp2ArQxkU6WdgIeM7dgEcvvRQxAUB/NIiVgn3OvVhR8McPHcZwItS2zLyemMRZn9wBXbutHzPpIp46vnjea47MpPFfv/0SAOBVbaQO2bnYxYqCqO4sAWA5V/sMX5xKIx7y40/fc1XH7xcP+aXaCmgPZ815aLY+dGgaAPAbr9/d9XvGw/Ie9KuFCnykTSfi1Pfs75ZEJICqyozNZVmoKtM6QnbRV8bMUCJ0nsRnN55z1+HRS6+Re38shBVJH+Zcpoi7vrjfaDEAaMvwdLGK337LJQi3GKXWiJhEWYZLBz+zR6s6btT69M+++zIA4DO/eFXbPQ5jYIfEaDga8mMgrkkHZmntxak0Lt+c6qrXkLZHIOvcag4tGvRjKBHGbKaIQlnBoXOruGZbf0/6cDwkT+YolLUHp99H+Iv3XY2/7jKTpxF29XTnFefddIQ040XuLsLoCNmD5g4AA7GgtFTIbz8/jYdfmsVNn3nMqE48Nq+lvu3rMAXSTEzi1Hv+vhMDUfgImF1dW8jxV48dw6NH5vCfbtqJ9+2b6MhWQGbkriK6RpbRomFFZTg8nW7bE70eLbtHjmzAV0XRoB9Xb+3DSr6Cy37/u3j65BKu7NJOjpaXL/fBCQC/cO1W3PqqzmoxWmFXT/d0D+2/zQzGQ45kz5nxnLsOv8DjvcoysRBWcnJkGbMOfOe9WqHN0dksAj7C9iY9WVoRkygdcAfEM1DMrZAZY7j/R6cAAL/2s51JCDGjolZedBkO+hD0+9AXDWI+q9l7ajGHfFnB5V1LXgGoDFJkg4JJlnnjZWs3onst6Y+F/VIj91aToHqBN2eT3RmS1z/0RXt37plidc0elN14zl3HeuQeQqYk58PMlauIBH3YNhjDQraMQlnBK7PaGLVgBy0H6pEauZs2/TalIpgxOfep1SLmMiX8wTuv6FhCiEtuUcw1dwDYPhTDs6dXUFFU3PLZHwAAruo2b5w3D5PgMIvlmnMfTUbwLx9+nfE9nvveLYlQAOWqKuW6LVQUY+UlCh65ZyTPUU3rVbCpHp37gIOFjRzPuetwnbTbQR0cvnxblbCpWigrSEWC+J23aZ3+3vDn38cjh+ewt4fsCECLLqXJHKZ0vbHU2iEmz5zWqmm7ycs3NHeJKw3u3PtjIbw0ncbtd//Y+P4lY92Ng4tJHLVnlmUAYO9EP/7sF6/Cp269bM2mZTfEJA4YMZ9bUSTD2n0mO3LnTb9SkR4j99j5G/R24zl3nZwRufcqy2gXgYxN1XxZi4Am9NFkPBquHyjdKXZE7tEQd+61Dcof6d0ru5mfyT8PGc6SMaZF7np0+fO6JswfQj/8nTd0nzcucY+g3rkDwHv3TeA/3byr5/eUaq8EWcYuzV2ELAMAizbXvpjxnLtOLRWyd1kGgJRc93xZQTQUwA5TJee79m7Gu67prEFUPTGJee5mB7SpL4LVQgW5krbByKfed9K9smarvCHZZUWrbeAO6L37tuLjb9I6VA8nQj1NC4oZPdIlRML6OQhbzDgxI7Onu/nBKYqETT3oa7JMb/6gUWqt3XjOXcdqKiR37isSnHuhUkU0qG34vfHSUVw8lsBn37e356goqqdCqhKGi3AHFAn6cak+OOQT3ziE5ydXcW6lgJv2NB8B2AgjcpeiYddSCwGtsvAdV49j53Acn3//tV1H7UAtEpYhIxUrCiJBX9djIFsRlzhgRIosY2ju8rNlAj7q2X4jtdZBzb03T3YBkisrCPl9PefhcllGxgZKvqwYewH33LkPpapqqfkSd0DFam9jBVthLrThrV3/9fkppAsVhPw+vPva7sbtRgJ+EMlzPtxWzq6RBB777df3/J61Hunrw1nGJfZ0l2FvOKANRJGuuRe1hnK9POAB00rewXRIL3LXyZWqPfVy58jU3Avl2vKWiCzrmDI7Q5plmWQkiG/859cC0EYB7p3o71r28unRk4zIvZbZI1LmkBe586IgkciUZQplFRHBsgwR2dIZcrVQ7TlTBjD3m/Kcu+NkS9WeM2UATQsM+EiK5i46pSwqcUg2r6LkD6BrJvqNqsJehzRo2T3ibS022KC0isw9gnxFEe4sDecuSUYS/TACtHstI3lgR7pQ6bqPez1avynPuTtOpmjtSU1EegsCORuqIp277Mg9FPAZshER4dd+dhciQR/ecsVYT++p9T+RGbmLO7dy9whkyDJy6ggYY1JkGUBLT5QduaeLFUv+ANBSa+1uJmjG09x1RDyp+2NBebJMUNxHxW84OdJB9bwb+iNv3IPfeP1FPe8TxCQNlDDn5ItC9h7BepFlKgqDojLh2TKAtqkqX5apWO5gORALerKMG0hbjNwBOf1lGGPIl6uCZRntvaTIMk0ckNUN4EJFYt64wHPr85E21FvWBqVgZ1mbqSv2WihIeHBykpGgkaooi3Sh2nMBE2cg5skyrkCL3K0vw0TLMqWqlost8qaWK8uowh1QLCwncm9UFCSCmKQBGDI2VH0+0toUC34YyXhwclI2RO6aLGNttdwXDUrrN9UJnnPXEfFhDsSCwp07j4CkaO5NBmlYQU6zKDmaOx8AIsNeKTKShMgd0AeMCD6/sh6cAJdl5DnNYkVBuaoKidxl9ZvqhLbOnYjuJaI5InrBdGyQiL5HREf1vwdM3/skER0jopeJ6K2yDBeJqjJkS9aXYf0SlmHcAcvJlpGVISE2ZpClufP/v2jnHpM0ak/WBmUiHEBW8Pk1F7OJJhkJIluSN42JtzZIWs6W4enRzkTvndyF9wF4W92xTwB4lDG2B8Cj+r9BRJcDuB3AFfrP/DURif90BZMpVcFY7x3gOP2xIEpVVaiWLcMBxYIyZRnx0aWsyVEZQTdxPdrQ6fWxKgL087uOZJlkRGurLGsQOZd8rF4X/UbVujO6e1vnzhh7HMBS3eHbANyvf30/gHeZjn+FMVZijJ0EcAzA9WJMlQdvzG85rzUmvs1n3pBlBGbLyNTcJejCsbBfyo2cLVYR9JPQXi0AH4AhR0aS4SxlTGOSUUPASeorbFnSDH9f3oGyVwaMqnX3Ru6NGGOMTQOA/jefHLAFwFnT6yb1Y+dBRHcR0X4i2j8/P9+jGWKw2ruZMxATvwzLS9DcwwEfiORky2i6sOBIWO85XhWsXWaKVSTCgZ5LzJsRl1B0VVVUlBVVirOUMUdVbraM3FF7oiJ3GcFeN4jeUG10lzQUxhhjdzPG9jHG9o2MjAg2ozuMD7PHjpCcvqj4ZZhxkwh07kR6up60VEjRmrucDeBsqWpEgSKRIXMUq3zzV3wOhJaNJEuWEW8vb8MrS8s2InfLe3D6jId1FrnPEtE4AOh/z+nHJwGYB2NuBTDVu3n2IMqB8g0UkcswGZE7oG2qysgdz8vIlpE0UIJH7qKJh8VH7rxRVsKiVNCIhIThLTXnLv788na6S5J6pac3eOT+IIA79a/vBPAt0/HbiShMRDsB7AHwtDUT5VObn+q+D5NrtzGBFaqAvIEdWsGVeFsB8f1PMsWK8M1UQM4GcC2alGCvhA1gmbIMH9EoawC1KFkmFvIj5Pc5prm3tZ6IHgDwegDDRDQJ4L8A+BMAXyOiDwI4A+C9AMAYe5GIvgbgJQBVAB9mjMnZ0hYIvxGtXoh8uShy1J6srANtSLbYj6aiqKgozOhXIgre0E105J4tVTHeFxH6noAWJFQUhnJV7bmFdD08sychwbkn9KIrxpiw/QdZK07A1Cs9K8u5a/ev1VUdEaFPUkuSTmhrPWPsjibfuqXJ6/8IwB9ZMcpuRF2IkaAf0aBfaA9nGUVMgBaViO6JnZewPwDAaMUsPnKvYs+onMgd0IKGUCAk5D15NGk1o6sRsVAAjPHuo2Lev1Cugkjs1ChOOOBHMhyQGrnHQv6uJoY1Q0ZLkk7xKlQhNt1Q+zDFa+7iJ9qI789hSEgSsmXM7y8KWRuqcQltf0Vt8jUiwR+eAldG+bKCWNAvPBOJM5gISWvKtZQrGxKrVZzsDOk5d9SiDBGZCFp/GYGRu4TRaoCc/hy1vQvxEhIgvi1ttliVInPEJLT9zRgbqnIid0DswzMvISXWzGBcnnOfXi1gc78YuW7AQVnGc+4QG2UMxINYEai5y9igBCRF7iV5jbgAsc6nWFFQVlQpG5QyIvesoE2+RvBEApGFTAXBMwjqGYqHpckyM6tFjKVEOXcvcncUkVFGf1Rsf5m8hIpPQJvqnimK7c/Bna/VrKN64hIqao3+IVIiYRmRewVEsDQtrBm10YDizm+uJLZNdT1D8RAWs3JSIVcKFSPd0ip8JS+rD04rPOcOsVFGv+DOkOb5qSJJRoJQVCb0hpa2oWrIBiI1bHnZJ7XRdeLsTRerSIQCwuU5oHZ+hUbukjpYcgYTWhAl2mkyxjS5TtBDfyAWREVh0vrgtMJz7hAbZQzoT2pVFXPRiR6xx0kZ/TkE6qySMntCAR+CfhJaRWnIHBKKgszZMqLQNn/laNgJCUVisq5bzlA8hIrCjIIjURQrKqoqE7ZxbdS+ODCRyXPuEDuAuj8WhMpqeclWWRUwRKQR3FGI1N1F5Qc3QmujKzByL+m2yozcBTrLXKlq7D2IprZhLfZBL3I0ZD21KlWxTlP0ddEvod9Up3jOHTzKEPVhiu0vky5U0BcT79x5kzSRnfXmMpoGOpIMC3tPjuhpQVmp2SfiI/dyVZWSMw6YVnFCnbtczV1WCwLR9QT9DrYg8Jw7tIhFlD4ous3naqFiVL6KxIjcC+Ju6PlMCf2xIMIBWdOCxEXCJZmNuELiI/eyIq7atZ6EcS2Ie9AvZsvCNiUbYbQgEFylKvqhX/MHnnN3hEJFEVYyL/JJzRiT5tx5tCZSllnKlTEk6YbW2iWIjYQBIOQX/yDy+wiRoE9o5F6qyIvc/T5CMhwQdi1UFBXZUlWqc+fvLTodkm8qi3LutZW8J8s4Qr4sLhWSX3QiZJl8WUFVZZKcO9fcxeaOy8qQ0JpxiY2EASAYkFNBGRfcabGkqAhJWBFxUtGgsFWczFURh0fus+mi0PcVXQnsae4OkxeaLaM3NRIw9ZxH/wMSNHcZ02xKVVWKJANokZTIzB4+tDgkoH9II2Jhv9Dsk3JVlWYroMl0oiL3kt7sTta1AGgZVMOJkATnLrZYLOj3IRkOeLKMEzDGkBcoy6QiQfhITOTOn/b9gvpcmIkEtfRCkQ6zVFWkSQd90ZBQTdiQZSTZKzxyl3huAR65C3Lu+rmVaS8AbOqLYGbV3c4dAPrjzrQg2PDOvVhRwZi4oQI+H6E/JqbvBXfuopoYmSEirQWBYIcpy1n2C+7RUZLs3GMhsT3SZWbLAFpQIkqiM5y7RFkGADalIphJi82W4Zq7yCrrwVhIWquEVmx4514rmRe3hBTV5lPmgAZAfPOwkkQH1B8NIldWDDnFKjxyD/okpRdGxfbukfngBMQ+PEtV+bIMAIylIphZLQh9z2ypimjQj6BACWwsFREuH3WC59wltNQdjIewLEBzr21MyblJRDcPK1XlbfoZ8ygFrTQqioqgn6SU8wPaaktkgY3MVEgAGEpo0aWIcv5SxSZZJhXBcr6CosDZupliRXhh2+b+KKZXPOduOyJ7uXO0Hs7Wb+xaBCQruhQbucuUDvoEp5TJ3qAU3WNIZiokoJXzl6uqkP4yNc1dbuS+SZ+iJTIqzhSrwpvJbeqLIFOqCk1e6ATPufMBEwJlmUFBUZvsTb9kWKzmLndDlUfuYqLhsqIiKNFZDsRCyJaqxmdoFemRe1xcUZARlEjW3LcMRAEAk8vipJlMUXwPHz7KUfTmbzs8584jd4HSx0A8hJV8xfISV3bWgejIvVSRuKEaFZsvLDty5+mrKwIeRlVFhaIyKQVXnMGEuKIgu2SZHUNxAMCpxZyw98yWxA9wGe/THkLTnnO3FxmyzEAsiLKiWm7zKTujQ7jmrsjLcxddDCI7Eh6I826A1u3lBVcy7R02Infr2Se2yTKpCEIBH84s5oW9Z6ZYEd4p1IvcHUKGLFO7sa1FQYZzlxRhpiJBrQpWQAYKY0yq5t4f1TV3QTKS/MhdXBuKsg154zxyFyEnyt4r4vh8hImBKE4Lde7iZZmxVAREwOSK2MyedvR89onoEiI6YPqTJqKPEdGnieic6fitIg0WjYwe5IMxMTdKqaogFPBJGzLML2IR0oz8VUYARMCqoHQ9O1ILATHFbLL3XgAYPYGEyDI25bkDwPahOE4viXXuomWZUMCHzX1RnBVoZyf0fPYZYy8zxvYyxvYCuA5AHsA39W9/jn+PMfaQADulwdvIxgT2njYid4s3tvTClai4gR1cOpBlr89H6IuKm08rXZYxIndxKbEy7Y0E/UiEA2I2VG1oP8DZPhTDmcWckBROVWX6UBTx7T4mBqM4s16cex23ADjOGDst6P1sQ8a4tUFBzl1mURAgdmCHsYkmKScf0DZVRWnuFWX9yDJ2lfMPxkNYFNAf3S57AWDbYAy5siLkIZoti+3lbmb7YHzdOvfbATxg+vdHiOggEd1LRAONfoCI7iKi/US0f35+XpAZ3bNaqCAZCcAvsJiFyzJWo6CyxEZcgNi2v4bOKtFh9sVCQjV3kVWI9URDfkSCPiHj1ezQ3AG9kElIKqR9zr3W1936Q0lGXxnOtqEY5jMlFGycpWr57BNRCMA7AfyTfugLAHYD2AtgGsBnG/0cY+xuxtg+xti+kZERq2b0TFpCv3T+sFgvkbtIzV2mztofDa4bzR3QoncREaUd2TIAMJoMCykIKlUV+H2EgMSHJ2dI3wheEPBQqo2JlCHLxAAAZ5fti95FnP23A3iWMTYLAIyxWcaYwhhTAfwdgOsF/A5ppIviZ5T6fISBWNBy299SRZF6Q/OHmohCJjuiS5Gae8kG5z4YD2FBRGqhrmHLzHMHtHxsEbnYsqtpzRiRuwA5SWrkrjt3kWmb7RDxCdwBkyRDROOm770bwAsCfoc0ZE06GoiFLC/Jy8r6i9zlN7daH5o7oEXC8xnrTsfYrJacfbK5P4JsqWpZppO94jRjZPkIiNyzNjh3kZk97bD0CRBRDMCbAXzDdPgzRHSIiA4CeAOAj1v5HbJJF6pIRcV/mIPxEJasyjISKz6B2igxEc247MiQ6Nc7LYrIy5edLQMAo8mIMTTcCmXJ9Q4co5LSYpMrrQ2F/EwZQOvjRCQmhTMteAqTmYFYEMlIAKcWxFXTtsOSV2OM5QEM1R37gCWLbGa1IF6WATTnfnQua+k9yooqtFtlPQG/D/2xoJg+OJJTIQFgJBkGY8BSvozRZMTSe8kuYgKA0VQYi9kSFJVZ2rC3I88d0CJ3AJhaLeCSTcme36dUVW3JcQe0+a+DsZCQDVUe5MgI9ogIu0YSOLFgzSd0w4avUJWV1zoQty7L8CImmQwnwoJ0YfkOaCSp6atzAgY0lKuqtPmpnNFkGCqznslhV/aJsMjdRs0dEJflM7NaRMBHRisG0eweieP4nH2R+4Z27owx5MpVJAS2HuAMxbW2v6rae3GF7CImQLNTbPqbvJXGiB6tzwt4GFUUuY24gJq9VqUZuyL30WQYPgKmLQ7AsFOWAbSOliI2VGdWixhLRaT1+N89ksBMuiikrXInbGjnni8rYExsARNnIBaCyqzp2XZkdAwnxUTuZUV+P5FRPXKfFxS5S3eWKX2lkbEYCduUChnw+zCWiuCcxR4odm6oAlpfHBEByvRq0egRL4PdIwkAwMl5e6L3De3cZcxL5PAqVSubqnYsb4fjISGRsK2yjEVnyRizaUNVjIxkpJlKXmkAWhvdExadj52aO6BdwyI2VGczRYyl5EgyAHDRqNai+Pi8Pbq759xRyxoRyaCAzpBliS10OcOJMDLFqlFh2it26MKRoB+pSMByeqEdm7+A+WFkVXO3Z/gFAFyyKYlXZjOW5ETbZZlEGKuFiuXBKJliVUpaNGfbYBx+H3nO3Q54XqtM524lopBdxARosgwgplUCILe3DKA5TGEatuRsmXDAj/5Y0PJKwy57AeDisSTyZQXTFipVndhQBaz38ckWq1J8AScU8GHbYMxz7naQs0GWsR65y99QBaw7dx5dyi8MiliP3G3aoAQ0aUaELBPwyRvmbWZTH5eSLDh3mzV3fg1b2TuqKioKFUVK6wEzdmbMbGjnbocs06vmrqhMy+iwKXK3uqlaqqogAoJ+yemFKQGRu00blID+MBJwbu2wFQBGEnpGkoVz7IQsA1gLUPjUtLiEzDkzu0cSOLmYg2JB9uoUz7lDjnOPBP2IhfxY6vGCK9uQWgjUxqtZdUA8bVPWYBHOSEIr6bfSv5ufW5ldITmjKTGRu229WpLWG3HZvaHKI3crxXjcF8hoPWBm+1Ac5aqKGQEN2tqxoZ07l2VkpEICWjpkr5G7XdIBv5mtyzLyKz4BzVkWKoqlXGE7ZZnxvghm0kVLkZodaZucIf6wtxK52665W1991vbf5MoyE4NaoZgdU5k2tHPPSIzcAb2/TI/RhF1zKGOhAGIhvwBZRpG+mQrUMlCsOB9DlrHhYTTeF4WiMkubqnakbXJCAa0lRa/XA2PMdlkmFQkg6CdLyQvZkt7uV3LkPjGgt/71nLtccqUq/D6S5kAHLbQg4KmFERscpla+bdG52xStjQqo+rRr+AUAbOnXIrUpCyX9djvL4UTv3SyrKoPK7Dm3HCLSpkhZuIaNiWySNffN/VEQAWeX5Q/L3uDOXUE85JemE1vpDGlX5A7w/jLuHizCGRURudspy+jNuKyU9NvR5MzMiIV+Q3YOxzYzFA9b21AtafebbFkmFPBhPBXBpBe5yyVbkpvXOhgP9byhWqzYF12KaB5mV3QpojDIzmwZEc247MyWAbQMql432O0cjm1mKBHCwjqQZQBtKpMd81Q3tHPPlapSctw5g/EQcmUFxUr31Z+1qkT5N8lwIiQmcrchWuuLBhHy+8RE7jZEw6lIAIlwwFK/FtudeyKEhR7Pr53zU80MJ8JYstA8zJBlQvY4dzvG7W1o554tVRGT6NytpGjxXi0RmyL3pVzJUkaHXZo7EelVqhY2KG2UZYgI430Ry7KMnc5yJBlGrqwgX+4+I8kpWWbQYnfTdKECIpsi94EYZtOlnoK+btjQzj1fVqRuoFgprijZVM4PAJv6IlCZtYZcdm76jVgcX8dlGTvy3AFgvN/abFK7nTufS7qQ6eW6dU6Wyff4QAKA5bw2btPKUJVO4emQVrtvtmNDO/dcqYq4xGWYMZm9h+VisWLfhupmI6PDmnRglwMaTYYxa7E8HrBPOtjcF7F0bu1MhQRM6aY96O4lG/eKzPBivF6j95VCBf0Sm4aZmRi0Jx1yQzt32RuqVi44O1MhebreOYubfnasMgDtYTS1Uuy5SrVi44YqAIymIljMlXue/VqqKrZnywC9ZSTZMbSlETyQ6jXXfSVfRl8sJNKkpmwd8CJ36eRKVcSkyjK8+rOXm8T+yP2chdzbUkWxLVrbOhBFtlRFutDbEtzODVVg7ezXXihVVFse8pxhCxWfdrYnNmP0cupxU3W1UMFAzJ7IfcjiKqNTNrhzV6Rmy8RCfkSCvp6iCTulg0Q4gL5ocN3IMnylMbnS27LWzg1VABjRH/K97hMUK4qtzt2QE9eTLGNhnwDQ2gXbJcuEAj6kIgEhg+lbYekTIKJTRHSIiA4Q0X792CARfY+Ijup/D4gxVSzlqoqyokpNfSIiDMV7yyE3bhJbpQ6rGR322Qr0njtuu3O3WHhVtHlDNejvvQXB+pVlKui3SZYBtGQLEeMtWyHiinkDY2wvY2yf/u9PAHiUMbYHwKP6v12HzF7uZoYTvfWX4RuqdqRCAsCWfmuzM+3sBFibTdrbzVFWtPbEARsyIwBrbXQZY9qD08bIHdCrVC1ly9gbucdCAUSCvp5kmaqiSp/CVI+owfStkPEJ3Abgfv3r+wG8S8LvsIxRtCA5r7XX/NtSVYXfRwjYpAtbidxVldkyWIQznAiDqPfUzaK+PyC7PTHHShvd2sa6/TJHL9kyeb0vusy9rGb02oKAD7G3S3MH9H5OFoquOsHqFcMAPExEzxDRXfqxMcbYNADof482+kEiuouI9hPR/vn5eYtmdA8fyTUgeSk2lAj3vKFqZ/SzuT+KdLGKTLHS9c/WZpLac0MH/T4MxUOY7bFPer6sIGZDJSInFtKqVHuJ3GsrOHud5XCyN9nAWBHbeH45msPs3rmv6M7dbllGduRu9RN4HWNsiohGAXyPiI50+oOMsbsB3A0A+/btkz+WpI6ac5f7tOY9LxhjXUWKdo8qGzNJHclId+fEiU20kWQE8z1G7oWygqjNMsdwItRTJMx7DNm5oQr03oKATzSy+/wCmtTRyzleyWvOvc/OyD0ewnK+DEVl0gqnLN2NjLEp/e85AN8EcD2AWSIaBwD97zmrRspg1aan9XA8jHJV7Xq4hN3pb7yVbm+5zfanv41aGJSdLyvSx6nVo1XVdv8wsrOYzcxwQmtBUCh3VyKfL1URC/ltmfdaz1Ai3FOjvtWC9jN2ZcsAmnNXmZZfL4uerxgiihNRkn8N4C0AXgDwIIA79ZfdCeBbVo2UAe+zbkfkDnSf01q0WZax0m3RiQwJK4On8xUFUZtlg5Fkb22Vi/qD0+7IfaTH2bo5myUvM0Px2iq5G5ZzXHO3V5YBrI0zbIcV7zEG4Akieh7A0wC+wxj7LoA/AfBmIjoK4M36v13HMl+KSX5aG/1lutw80Tb97HWWgMXI3caH0WhK2/DrpdlZoVxFzIHsk17OrexRkM3gVardPuzz5artqyLOUCLU0yq5prnbu6EKdO8XuqHnK4YxdgLA1Q2OLwK4xYpRdrCQLWEgFpSejcI7Q3b7hM6VFFtv6L5oEEE/9ZSBwgcd2KmzjqUiUFSGpVzZiDI7JV9WsCll340MaDLHaqHSdYO1dNGewc319Fqlmis5GblrNi/lyl3tG63myyBC13tNVhhxeeS+rjm7XMBWfZ6hTIZ77AyZKVak9r2ph4h6ji7TeoZNykbNctSQkbp/GOXLCqIhp2SO7q4DPrg5aeO1AJjTN3uI3G0+t5xaZW1353ilYF9HSE6tY6y8yH3DOvfJ5bzRwEcmvOdFtx9ipli1PVobSUV6cu68ZiAVtc/eET5LtQfdXXNA9mvuQPeyF5cY7IwqgVoU3G0hU64st6VHK3pdbSzn7esIyenXHyYyq1Q3rHNfypWNi0EmoYAPyUig6/zbTMl+595rK920rlmmbHRA/ME82UPhlaORe5fOndcd2K25hwI+9EWDXWvC2nQzZyL3XlfJdnaE5Ph8ZHnASNvfIe2dXYyiMlu7wA0nwt07d5tlGUCbEHN2qdB1tkEtcrdXlokEfTi9kOvq5xhjjqVCAt33SOf7GXZvAAP6DOAur1stFdKZyH0w3puUtGpjL3czImYXt2JDOvd0oQLG7KtI0/pIdP4hVhUVxYoqfRJ7PduHYihUlK6lg4wuHdjpgIgIO4biOLXYnXMvKyoUldnugLjM0e25zZeriAadyRvvxbnnyopjmrux2ujSYa7k7Qv0zIiYXdyKDencjerUuE39mxPdLb/yeuGK3dHltiFtg/l0lxNitIlW9jugrQMxTHbZgz7PI2GbHVAooHVa7N6527/K4AzEeojcy3LnErdjqAeHuZwv29p6gDMUl9tfZoM6d3t7SQwlwl19iDy32e7okvdJ73bep6az2n9Db+rrfo+APzjtdu6A3mmxy6iy4MD+AIeXyHdKuaqiojDHIndAqwjv5hw70RGSM9xj581O2ZDOfcWmpmGcYX1522nBDddZbdeFexyvlisrtu8PAMBYMoLlfMUoouqEvEMPTqC3wd65chWxoDOR8IB+3Xa6B8OHUzuluQNaCmc3+1u8jsAJWWYoEUah0vtQ73ZsSOfOI3fbxmolwl31keAftt3pev0xrZCpawckeVxhM8ZS3adDGi1pnYjck9230c2XFUfOLaBF7hWFdVzxyZuGOSUjAeh6OA6/J52QZYZ5Xr6k6H1jOnfeVyZulyzT3ZQYI0PC5puEFzJ1Wxikae4ORO59mnPvRpqpOXf77R3uoUisUFYceRABtfujU93dyVURZzgRxkq+YgxBb8eyAx0hOUZeviTdfUM696V8GQEf2Vb1122KlpM9sXspZMqVndHceZvibvq616QDZyL3fFkxPt9OyJUVRB2SZYa6dO48wndCouPwQKpTm53oCMnpNS+/Uzakc1/Rd8dtm8TT5YeYK9szArARvbQgyEseNN6MsWTvkbsT0sFIDxWUBQcbcdWqqzuVE52TvDjDXQ73Xsnb3xGSY2UQeSdsSOe+lCtj0KY0SKD7CMhJBzSa6t65Z0vO9BPpjwURCvgw24WMxCN3u1v+Ar21IMg7KMt0O6vWrrnEreg2kKplzjmxodpba5JO2bDO3c4NlP5YCD7q/EN0KhUS0KLLxVy5Y80SsH9sHYeIMJYKY7aL1E3jwemAwxzuIRsp76AsM6LPqp3pcGXkhsh9qMvVkRMdITnhgB/JSEBaIdOGdO4z6SI26ZkWduDX+0gsrAPtkkdrnUY+jDHkylUkHJIOxpKRLjV3vT2xQ5o70LnjcfrcBvw+DCfCmOvQuTspJ3K6HY6zUqggFbG3I6SZXmofOmVdO/e5TBFffOoUprpoHqWqDDOrRYz32+fcAT6ZvfPIPRr0O3LB1YY0dHZDFysqGINjVYljqUjXsozfRwhJ7uPfiMG4toLrNHLPlxUw5qyzHEuFO4/cHar+NZMMBxAK+LrS3J3Icef8yXuuwm/eskfKe69v554u4fe/9SIOnVvt+GcWciVUFIbNffLb/ZrppgVBtlS1vQsgZzTV3SxVI1pz6IYeS0W6lmViQb9tm+lm/D7CUKLzXHc3aNibUp2vjHIuKGIiIgzHO29BsOxAR0gz1+8cxMVjSSnvva6d+yY9z3mmi5t7ekV77XifzZF7F50hsyVnKj6B7mepOrk/AGiRZa6sdF5o41CrBE432UhuSC0cTUU6l2VKVUSCPsckDs5wsvN2H051hLSDde3cB2MhBP3U8bIRAKZXNQlnc7/NkXs81PFSMetAu18Ol2U6TS+stUpwTpYBurPXqVURoD08O46EHT63gLansZgrd9Tiwak2FPV0c685LcvIZF07d5+PMJaKdBW5vzyTBRGwYzgu0bLzGU6EkClWUax0cJOUnOsEGApom2idnlOjVYJTG6rcuXdob8bhyH3LQBTnOtwjypacPbeA1pwN6Eymc7KXu5mhRLjzDVWHOkLawbp27oAmr/BovBMOT6exczhue4Qxmuxcy86Uqrb3cjezpT/SsQPKOVjOD5iqVDvcVM2VqrbPIzUzMRDDUq7cUZWqk5XKHP7w7KRTaM7BnHwzw7pzb9fwrKqoSDvUEdIOenbuRDRBRI8R0WEiepGIPqof/zQRnSOiA/qfW8WZez5jXWz4AMC5lQImbBiMXQ/vg9KJhJQrOZf+BgDjfdGOM5DyDkeXfN9laqUz554tOlfxCdTGA55dbt8z3w2phbwNdCfXg9P7GZzhRAhl3XG3YklvGsbTJy80rETuVQD/D2PsMgA3APgwEV2uf+9zjLG9+p+HLFvZAh65d9qWdHq1gM02p0ECMPLqO5E7nMyWAWrSQSfn1IjcHSq0iYUCGE6EcWaxswEjWYdXRRODWmBxdqm9s3TDhuq44dzbX7fpYgUpB69bTqeVn7wbox2zlJ2gZ+fOGJtmjD2rf50BcBjAFlGGdcqmviiKFRWr+pDmVhQrChayZdvTIIGac+9k4y/rcAS0YyiGYkXtaEVkDMeOOmfv9qEYTi91Nm4v6/CqaIIP9u4kcneB5p4IB9AXDXYUuacL7pA4jG6LbXR3npLqOfcWENEOANcA+Il+6CNEdJCI7iWigSY/cxcR7Sei/fPz8z3/biMi7sBp8qh53OZMGUBzfpGgr23kXq6qKFdVJBzUWflm88kOhk/zh6oT5duc7UMxnFpo7ywZY45LB4PxEOIhP850MMowy7NlHN6k3Nzf2SZwulixdUh6M/i82nYZM3z/i6f/XmhYdu5ElADwdQAfY4ylAXwBwG4AewFMA/hso59jjN3NGNvHGNs3MjLS8+/nmmsnGz5TPA3S5hx3QCuu2JSKtH0IcWfpRCMjzo4hzbl3Mnw6XawgGQ44mtu8cyiOmXQRhXLrTKRCRUFVZY5KXkSEbUNxnO5ARsoUK47Mpq1nS3+kbeSuqgzpQsUVkfu4sQ/T2ma+irazFYmdWHLuRBSE5ti/xBj7BgAwxmYZYwpjTAXwdwCut25mc7opZDIKmByI3AG++dvazmUHJ8NwNvdHEQr4cKrDyN3paI2vNNo9jHikxjOXnGLHUKyjB+dKvmLbQJlWbOmPYnK59R5MrlyFyoCUgys4Tn8siGQ40HZ4+ly6iFQk4NiMWtlYyZYhAPcAOMwY+wvT8XHTy94N4IXezWvPaDIMH3W2m8+XlnZXp3I29bWP3I0pUQ46d7+PMDEQ7Si6TLvAue/UnfvpNg6T7yHw9Emn2D4Ux9mlfNuZuku5sqPXAWfHcBzZUrVlGi/PTHFy74VDRNg6GGsrfc2ki0aq54WIlU/idQA+AOAQER3Qj/2/AO4gor0AGIBTAH7Nwu9oS9Dvw+b+aEca5uRyHqPJMCJBh9L2UhHMrpbAGGva28TJ/tJmdgzFO4ouVwsV9Dl8Q9f2CNrfzIDzy/AdQzFUFIaplYKRPdOIlXzZFZH7RaMJAMCxuazRe6geHpT0RZ23FwC2DUZxfL79w36TQ4GeHfR8VzLGngDQyENJTX1shOaI2jv3s0utbybZbOqLoKyoWMyVm+7Q84G9Tt/U24fi+NHxxZYPIkBz7lyjd4pEWEuHbCcj8R4pzRyUXWwf4iuNfMvrcTlfsb2SuhF7RrXGVsfms3jtRcMNX8P3vJxaFdezbTCG77883/L6nUsXsXuk8f/nQmDdV6gCeipcB1Hm5EreKCJxgu1D2o3cylZeWOF0v4sdwzEUKkrbitr5TMnoAe8kO4ZiONlWlikiEvQ5nou9Y1i7DtqtjJbz7pBlxlJhJMIBHJvLNn0Nl0Xt7tnUjInBGEpVten1q6oMc5mS4xKdTC4I575jKI6VfMWIehtRVVRMrxQdde67hrXlbavl4tmlAgZiQcd7dGw3Mmaar4iKFQXL+YrjMgegSTPtIveZdAmbUhFH2v2aGUtGEA74Wj7kc6UqMsWqK9L0iAi7RxMtnfu5lYLel8j5hxFQKxZrJtcu5sqoquyClmUuCOfOI+JWjujUYg5VlRn6oRNsHYgi6KeW+eMn5rPYNeKcjZzdI5pzPzqXafoanqHkhk2pncNxzGVKLXu2zKaLjksygNbwbvtQrO31CtQ2i53mopH2zn1Lf9TxBydnWxvnzrPWnM6ckskF4dy5w35ltrkjemla+94lYylbbGpEwO/DtsEYTsw3v0kmlwvY7uC+AGdLfxTJSACHp9NNX8Ozj7Y60Kunnk5y8+dclB2xfaj1SuOs7pS2ueBaALR7bC5TaloJfm65YPShcQMTAzEE/YRXZhvfa3yPwIlWJHZxQTj37UNxRII+HJlu7txfmcnA7yPsHnU2Eto5nMCJJrJMRVExvVpwVDriEBEu25TC4Rbn9Nwyd+7O22vo2E0yZhhjWuqbC2QOANg9ksCpxRzK1caDyN22QbnHlDHTiKkVZ3o2NSMU8OHisSRenGo8pW3KSIt2/tqVxQXh3P0+wiWbUi2jzJMLOUwMRBEOOFuwsHtEq05slOM8s1qEyrTGXW7gsvEkjkynoTbJx55czsNHcIVuuWs4Ab+PcGSm8TWQLlZRrKiusBUALt+cQkVhTWWvmXQRIb8Pgy5IhQRqq+PjDZx7qapgLlPCln53rDI4V2xO4cWpdMPiqyl9j2DIJedXBheEcweAV21J4eDkStNI6MRCzhX65a6ROMqKakS9ZnhFnVtuksvGU8iVlabtaSdXChjviyLowLDpeqIhPy4ZS+LA2ZWG33dLGiTnis2aPPjiVOOH0exqEWN9Yddo2BODMYQCvoYPoxmXShzXbBvAUq7ccLVxbqWA8b6I460dZOL8XSmIm/aMIFdW8OyZ5fO+p6oMpxZyrtio3KlnzBybP/8m4Z0CJwbdEbm/amsfADR1mJMu01mvnujH82dXGq40eAGTW2SZnUNxxEN+HJpsLBtMrxZdkYXE8fsIl42n8NyZlfO+xwMVt6w4OTdfrPWs+sEr5zcmPDaXxW4X+AOZXDDO/bW7hxDwUcMPcjZTRKGiuCJyv3xzCj4CDpw9/6Y+u1wAkXt0wEs3pZAIB/DTU0sNv39uueCqG3rvRB/SxWrDTVU3ZfYAWsbMVVv7mz44p1YLrskZ57x29xAOnF05LyOJb6y76UEPaPZcuimJbx+cXnO8oqg4Pp/FxWNJhyyzhwvGuScjQezbMYCHX5w5T2Pjy7JdLnDuiXAAl25K4dnT568wJpfzGE9FEAq442Px+wjXbh/A/lPn21qsKJhaLThenWrmmm1ad+n9Dc7tmSVtf8BNDvOabf04PJ0+b66uojJMrxRd5yxft3sYVZXhxycW1xw/t6IFJW7ZzzDzi9dtxYGzK2s2Vo/NZVFRGC7d5Dn3dcO79m7B8fkcnq9b6vKN1kvHnUuDNHPd9gE8d2b5vE3V43NZo3jILbx6+wBens2clwJ3ciEHxrQ9BLewZzSBoXgIPz6+eN73Ti7kDN3YLVy/cxBVleGpOmc5lymiqjJXrYoAYN+OAfTHgvj6s5Nrjh+by2Jzn/PJCo14774JxEJ+3PPESeMYl26v2dbvkFX24J4rXQC3XjWOaNCPLz51as3xl6bS2JSKuCbz4LrtA8iVlTXRRFVRcWQmY2y0uYXrdgyAMZy30uDpnG6QujhEhBt2DeGpE4vnrd5OumRD3cyNu4cQD/nxyEuza47zIje35LhzIkE/3rdvAg+/OLumdfXh6TQuc0ngVE9fNIj37ZvAgwemMK3Pc/jRsUWMJMOuO7+iuaCceyoSxB3Xb8O3Dkytmal5cHIVV27pc9Cytdx88QiCfsKDB6aMY0+fWkKpqrrKTgC4ZmIA8ZD/PN3yuF6I5TaHedOeYUyvFtes3lSV4eRCzlUSEgCEA37cfPEIHjk8u2YV95KeQXO5Cx3m+6/fhqrK8JWnzwLQph0dn89h74S7rlszH/yZnVAZwz88eQqFsoLHXp7DWy4fc00mkiwuKOcOAL/2s7vg9xG+8INjALQl2ImFHG7YNeiwZTUG4yG86bIx/POzk8b0oL9+7Dg2pSJ425WbHLZuLdGQH7ddswXfPjiFVb0dMWMMj708h4tGE66Ydm/m56/ejEQ4sGb19sNjC8iXFeyd6HfMrma88+rNmE2X8O2D2oO+qqj49sFpbEpFMOTC2Z47huO4+eIRfPGpU1jMlvCxrxwAALzh0lFnDWvBxGAM77pmC+5+/AQu+/3vIl9WcOurxtv/4DrngnPuY6kI/sO+CfzzM5N4ZTaDz33vFQzGQ7jj+m1Om7aGD920Eyv5Cu554gSWc2X86PgC3rtvq2O95lvx/uu3oVRV8dff1x6YPzm5hOfOrODd19g+D70tiXAA77l2C/71+SkcntYKWP7no0cxkgzjTZePOW3eebz1ik3YPRLHPzx5CgDw7y/O4sDZFfzG63c7a1gLPvn2S5EuVnDTZx7DE8cW8OE37MYVm90buQPAf3nHFfhZPTXytr2bceOuIYctko+7wi5B/Mbrd+PbB6fwls89DgD4vZ+7zHUR5nXbB/GWy8fw5w+/gucnV6Ey4M0udD4AcOWWPtz+6gn87eMnEAr4MLlcQDIcwK++bqfTpjXko2+6GN85NIOPf/UAfvftl2L/6WX8wTuvQMJl1wCgpUTecf02/OF3DuNvf3AcDx2axsRgFP/xhu1Om9aUy8ZT+K+3XYlPfuMQJgaj+M+vv8hpk9rSFw3i/l+VOvHTdVCruYh2sW/fPrZ//36h7zmzWsSnvnkIi7kyvnLXDa6MiI/PZ/GO//kE8mUFl4+n8J3f/BnX6oDZUhUfuv+n+PEJLef9zhu34w9uu9Jhq5rzyEuz+NAXtWtqOBHGD3/nDa6dlVmuqrjrf+3H91/WajT+x+17cdte962K6jm5kEMiHHBFW+KNChE9wxjb1/B7F6pzXy+cWczj689O4h1Xj+OiUXfn3TLG8NChGTxzehkff/MeJF0wDLkVTx1fxL8enMI7r96MG1y+DK8qKv7pmUkwBrz/Ne6SED3ci+fcPTw8PC5AWjn3C25D1cPDw8PDc+4eHh4eFyTSnDsRvY2IXiaiY0T0CVm/x8PDw8PjfKQ4dyLyA/grAG8HcDmAO4jochm/y8PDw8PjfGRF7tcDOMYYO8EYKwP4CoDbJP0uDw8PD486ZDn3LQDOmv49qR8zIKK7iGg/Ee2fnz+/B7uHh4eHR+/Icu6NKnHW5Fwyxu5mjO1jjO0bGRmRZIaHh4fHxkSWc58EMGH691YAU01e6+Hh4eEhGClFTEQUAPAKgFsAnAPwUwDvZ4y92OT18wBOW/iVwwAWLPy8DNxoE+DZ1S1utMuNNgGeXd0iwq7tjLGG0oeUTkqMsSoRfQTAvwPwA7i3mWPXX29JlyGi/c2qtJzCjTYBnl3d4ka73GgT4NnVLbLtktYmjzH2EICHZL2/h4eHh0dzvApVDw8PjwuQC8W53+20AQ1wo02AZ1e3uNEuN9oEeHZ1i1S7XNEV0sPDw8NDLBdK5O7h4eHhYcJz7h4eHh4XIOvauTvVeZKIJojoMSI6TEQvEtFH9eOfJqJzRHRA/3Or6Wc+qdv5MhG9VaJtp4jokP779+vHBonoe0R0VP97wE67iOgS0zk5QERpIvqYE+eLiO4lojkiesF0rOvzQ0TX6ef5GBH9JVmcj9jErj8joiNEdJCIvklE/frxHURUMJ23v5FhVxObuv7MbDpXXzXZdIqIDujHbTlX+vs18wvOXF+MsXX5B1r+/HEAuwCEADwP4HKbfvc4gGv1r5PQCrYuB/BpAL/d4PWX6/aFAezU7fZLsu0UgOG6Y58B8An9608A+FO77ar73GYAbHfifAG4GcC1AF6wcn4APA3gRmitNv4NwNsl2PUWAAH96z812bXD/Lq69xFmVxObuv7M7DhXdd//LIDft/Nc6e/XzC84cn2t58jdsc6TjLFpxtiz+tcZAIdR1xitjtsAfIUxVmKMnQRwDJr9dnEbgPv1r+8H8C4H7boFwHHGWKuKZGl2McYeB7DU4Pd1fH6IaBxAijH2FNPuxC+afkaYXYyxhxljVf2fP4bWxqMpou1qcq6a4ei54ugR7vsAPNDqPSTZ1cwvOHJ9rWfn3rbzpB0Q0Q4A1wD4iX7oI/oy+l7T8stOWxmAh4noGSK6Sz82xhibBrQLEMCoA3ZxbsfaG8/p8wV0f3626F/bZR8A/Cq0CI6zk4ieI6IfENFN+jG77OrmM7P7XN0EYJYxdtR0zPZzVecXHLm+1rNzb9t5UroBRAkAXwfwMcZYGsAXAOwGsBfANLTlIWCvra9jjF0LbVDKh4no5havtfUcElEIwDsB/JN+yA3nqxXN7LD7vH0KQBXAl/RD0wC2McauAfBbAL5MRCmb7Or2M7P7s7wDa4MH289VA7/Q9KVNbBBi23p27o52niSiILQP8EuMsW8AAGNsljGmMMZUAH+HmpRgm62MsSn97zkA39RtmNWXenw5Ome3XTpvB/AsY2xWt9Hx86XT7fmZxFqJRJp9RHQngJ8H8Ev6Eh36Mn5R//oZaFrtxXbY1cNnZue5CgD4BQBfNdlr67lq5Bfg0PW1np37TwHsIaKdekR4O4AH7fjFuq53D4DDjLG/MB0fN73s3QD4bv6DAG4nojAR7QSwB9qGiWi74kSU5F9D25B7Qf/9d+ovuxPAt+y0y8SaqMrp82Wiq/OjL60zRHSDfi38sulnhEFEbwPwuwDeyRjLm46PkDbKEkS0S7frhB12dfuZ2XWudN4E4AhjzJA07DxXzfwCnLq+rOwOO/0HwK3QdqSPA/iUjb/3Z6Atkw4COKD/uRXA/wJwSD/+IIBx0898SrfzZVjclW9h1y5ou+/PA3iRnxMAQwAeBXBU/3vQTrv03xMDsAigz3TM9vMF7eEyDaACLUL6YC/nB8A+aI7tOIDPQ6/2FmzXMWiaLL/G/kZ/7Xv0z/d5AM8CeIcMu5rY1PVnZse50o/fB+DX615ry7nS36+ZX3Dk+vLaD3h4eHhcgKxnWcbDw8PDowmec/fw8PC4APGcu4eHh8cFiOfcPTw8PC5APOfu4eHhcQHiOXcPDw+PCxDPuXt4eHhcgPwftJlWvVFtdb0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_week = two_week[288*6:288*13]\n",
    "plt.plot(one_week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2304\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa8b69e32b0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oneweek_plusaday = two_week[288*5:288*13] # 8天 288*8=2304\n",
    "print(len(oneweek_plusaday))\n",
    "plt.plot(oneweek_plusaday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2880\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa8bb2d4ba8>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oneweek_plus3day = two_week[288*5:288*15] # 8天 288*8=2304\n",
    "print(len(oneweek_plus3day))\n",
    "plt.plot(oneweek_plus3day)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "#### predict plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Input</th>\n",
       "      <th>Predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>17.126684</td>\n",
       "      <td>4.481884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>17.772238</td>\n",
       "      <td>4.617570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>18.643532</td>\n",
       "      <td>7.977384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>19.536276</td>\n",
       "      <td>8.733210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>20.446896</td>\n",
       "      <td>10.945611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2443</th>\n",
       "      <td>2443</td>\n",
       "      <td>150.578920</td>\n",
       "      <td>131.661680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2444</th>\n",
       "      <td>2444</td>\n",
       "      <td>149.149100</td>\n",
       "      <td>130.573970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2445</th>\n",
       "      <td>2445</td>\n",
       "      <td>147.457580</td>\n",
       "      <td>129.605530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2446</th>\n",
       "      <td>2446</td>\n",
       "      <td>145.047990</td>\n",
       "      <td>128.549770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2447</th>\n",
       "      <td>2447</td>\n",
       "      <td>142.040000</td>\n",
       "      <td>125.280610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2448 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0       Input     Predict\n",
       "0              0   17.126684    4.481884\n",
       "1              1   17.772238    4.617570\n",
       "2              2   18.643532    7.977384\n",
       "3              3   19.536276    8.733210\n",
       "4              4   20.446896   10.945611\n",
       "...          ...         ...         ...\n",
       "2443        2443  150.578920  131.661680\n",
       "2444        2444  149.149100  130.573970\n",
       "2445        2445  147.457580  129.605530\n",
       "2446        2446  145.047990  128.549770\n",
       "2447        2447  142.040000  125.280610\n",
       "\n",
       "[2448 rows x 3 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_len = 2\n",
    "prediction_len = 1\n",
    "folder = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_plot/'\n",
    "week_len = 288*7\n",
    "file = folder + f'{word_len}_{prediction_len}_plot.csv'\n",
    "df = pd.read_csv(file)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "input = df['Input']\n",
    "predict = df['Predict']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa8b93155f8>]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(input)\n",
    "plt.plot(predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file name: /Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_plot/24_1_plot.csv\n"
     ]
    }
   ],
   "source": [
    "word_len = 24\n",
    "prediction_len = 1\n",
    "folder = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_plot/'\n",
    "save_path = folder + f'predict_aweek_excel/{word_len}_{prediction_len}.xlsx'\n",
    "\n",
    "week_len = 288*7\n",
    "file = folder + f'{word_len}_{prediction_len}_plot.csv'\n",
    "print(f'file name: {file}')\n",
    "df = pd.read_csv(file)\n",
    "df1 = df.iloc[71:]\n",
    "df1_1 = df1.iloc[:2016]\n",
    "\n",
    "df1_1.to_excel(save_path, index=False, columns=['Input', 'Predict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file name: /Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_plot/24_6_plot.csv\n"
     ]
    }
   ],
   "source": [
    "word_len = 24\n",
    "prediction_len = 6\n",
    "folder = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_plot/'\n",
    "save_path = folder + f'predict_aweek_excel/{word_len}_{prediction_len}.xlsx'\n",
    "\n",
    "week_len = 288*7\n",
    "file = folder + f'{word_len}_{prediction_len}_plot.csv'\n",
    "print(f'file name: {file}')\n",
    "df = pd.read_csv(file)\n",
    "df6 = df.iloc[66:]\n",
    "df6_1 = df6.iloc[:2016]\n",
    "df6_1.to_excel(save_path, index=False, columns=['Input', 'Predict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file name: /Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_plot/24_12_plot.csv\n"
     ]
    }
   ],
   "source": [
    "word_len = 24\n",
    "prediction_len = 12\n",
    "folder = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_plot/'\n",
    "save_path = folder + f'predict_aweek_excel/{word_len}_{prediction_len}.xlsx'\n",
    "\n",
    "week_len = 288*7\n",
    "file = folder + f'{word_len}_{prediction_len}_plot.csv'\n",
    "print(f'file name: {file}')\n",
    "df = pd.read_csv(file)\n",
    "df12 = df.iloc[60:]\n",
    "df12_1 = df6.iloc[:2016]\n",
    "df12_1.to_excel(save_path, index=False, columns=['Input', 'Predict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file name: /Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_plot/24_36_plot.csv\n"
     ]
    }
   ],
   "source": [
    "word_len = 24\n",
    "prediction_len = 36\n",
    "folder = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_plot/'\n",
    "save_path = folder + f'predict_aweek_excel/{word_len}_{prediction_len}.xlsx'\n",
    "week_len = 288*7\n",
    "file = folder + f'{word_len}_{prediction_len}_plot.csv'\n",
    "print(f'file name: {file}')\n",
    "df = pd.read_csv(file)\n",
    "df36 = df.iloc[36:]\n",
    "df36_1 = df6.iloc[:2016]\n",
    "\n",
    "df36_1.to_excel(save_path, index=False, columns=['Input', 'Predict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file name: /Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_plot/24_72_plot.csv\n"
     ]
    }
   ],
   "source": [
    "word_len = 24\n",
    "prediction_len = 72\n",
    "folder = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_plot/'\n",
    "save_path = folder + f'predict_aweek_excel/{word_len}_{prediction_len}.xlsx'\n",
    "week_len = 288*7\n",
    "file = folder + f'{word_len}_{prediction_len}_plot.csv'\n",
    "print(f'file name: {file}')\n",
    "df = pd.read_csv(file)\n",
    "df_1 = df.iloc[:2016]\n",
    "df_1.to_excel(save_path, index=False, columns=['Input', 'Predict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2304"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "288*8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "for word_len in [1,2,6,12,24,'lstm']:\n",
    "    for prediction_len in [1,6,12,36,72]:\n",
    "        folder = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/prediction_plot/predict_aweek_excel/'\n",
    "        file = folder + f'{word_len}_{prediction_len}.xlsx'\n",
    "        save_path = folder + f'predict_2016/{word_len}_{prediction_len}.xlsx'\n",
    "\n",
    "        wb = openpyxl.load_workbook(file)\n",
    "        ws = wb.active\n",
    "        for row in range(2, 290):\n",
    "            ws.cell(row=row, column=3, value=ws.cell(row=row+1152, column=1).value)\n",
    "            ws.cell(row=row, column=4, value=ws.cell(row=row+1152, column=2).value)\n",
    "\n",
    "        wb.save(save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:pytorch1.6.0] *",
   "language": "python",
   "name": "conda-env-pytorch1.6.0-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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